
#Importing Librarires
import numpy as np
import pandas as pd
import os
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline # for showing the figures in the Notebook itself

# We dont Probably need the Gridlines. Do we? If yes comment this line
sns.set(style="ticks")

flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"] # defining the colour palette
flatui = sns.color_palette(flatui)

UsageError: unrecognized arguments: # for showing the figures in the Notebook itself


from wordcloud import WordCloud  # importing the worldcloud module. Wordcloud uses the text sizes to represent the frequency of the text


df=pd.read_csv("FIFA_data.csv") # reading the dataset


df.head(10) # having a look at the dataset, first 10 rows


df.shape # checking the number of rows and columns in the dataset

(18207, 89)


df.info() #Printing a concise summary of the DataFrame.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 89 columns):
Unnamed: 0                  18207 non-null int64
ID                          18207 non-null int64
Name                        18207 non-null object
Age                         18207 non-null int64
Photo                       18207 non-null object
Nationality                 18207 non-null object
Flag                        18207 non-null object
Overall                     18207 non-null int64
Potential                   18207 non-null int64
Club                        17966 non-null object
Club Logo                   18207 non-null object
Value                       18207 non-null object
Wage                        18207 non-null object
Special                     18207 non-null int64
Preferred Foot              18159 non-null object
International Reputation    18159 non-null float64
Weak Foot                   18159 non-null float64
Skill Moves                 18159 non-null float64
Work Rate                   18159 non-null object
Body Type                   18159 non-null object
Real Face                   18159 non-null object
Position                    18147 non-null object
Jersey Number               18147 non-null float64
Joined                      16654 non-null object
Loaned From                 1264 non-null object
Contract Valid Until        17918 non-null object
Height                      18159 non-null object
Weight                      18159 non-null object
LS                          16122 non-null object
ST                          16122 non-null object
RS                          16122 non-null object
LW                          16122 non-null object
LF                          16122 non-null object
CF                          16122 non-null object
RF                          16122 non-null object
RW                          16122 non-null object
LAM                         16122 non-null object
CAM                         16122 non-null object
RAM                         16122 non-null object
LM                          16122 non-null object
LCM                         16122 non-null object
CM                          16122 non-null object
RCM                         16122 non-null object
RM                          16122 non-null object
LWB                         16122 non-null object
LDM                         16122 non-null object
CDM                         16122 non-null object
RDM                         16122 non-null object
RWB                         16122 non-null object
LB                          16122 non-null object
LCB                         16122 non-null object
CB                          16122 non-null object
RCB                         16122 non-null object
RB                          16122 non-null object
Crossing                    18159 non-null float64
Finishing                   18159 non-null float64
HeadingAccuracy             18159 non-null float64
ShortPassing                18159 non-null float64
Volleys                     18159 non-null float64
Dribbling                   18159 non-null float64
Curve                       18159 non-null float64
FKAccuracy                  18159 non-null float64
LongPassing                 18159 non-null float64
BallControl                 18159 non-null float64
Acceleration                18159 non-null float64
SprintSpeed                 18159 non-null float64
Agility                     18159 non-null float64
Reactions                   18159 non-null float64
Balance                     18159 non-null float64
ShotPower                   18159 non-null float64
Jumping                     18159 non-null float64
Stamina                     18159 non-null float64
Strength                    18159 non-null float64
LongShots                   18159 non-null float64
Aggression                  18159 non-null float64
Interceptions               18159 non-null float64
Positioning                 18159 non-null float64
Vision                      18159 non-null float64
Penalties                   18159 non-null float64
Composure                   18159 non-null float64
Marking                     18159 non-null float64
StandingTackle              18159 non-null float64
SlidingTackle               18159 non-null float64
GKDiving                    18159 non-null float64
GKHandling                  18159 non-null float64
GKKicking                   18159 non-null float64
GKPositioning               18159 non-null float64
GKReflexes                  18159 non-null float64
Release Clause              16643 non-null object
dtypes: float64(38), int64(6), object(45)
memory usage: 12.4+ MB


df.isnull().sum() # checking the count of the missing values in each column

Unnamed: 0                      0
ID                              0
Name                            0
Age                             0
Photo                           0
Nationality                     0
Flag                            0
Overall                         0
Potential                       0
Club                          241
Club Logo                       0
Value                           0
Wage                            0
Special                         0
Preferred Foot                 48
International Reputation       48
Weak Foot                      48
Skill Moves                    48
Work Rate                      48
Body Type                      48
Real Face                      48
Position                       60
Jersey Number                  60
Joined                       1553
Loaned From                 16943
Contract Valid Until          289
Height                         48
Weight                         48
LS                           2085
ST                           2085
                            ...  
Dribbling                      48
Curve                          48
FKAccuracy                     48
LongPassing                    48
BallControl                    48
Acceleration                   48
SprintSpeed                    48
Agility                        48
Reactions                      48
Balance                        48
ShotPower                      48
Jumping                        48
Stamina                        48
Strength                       48
LongShots                      48
Aggression                     48
Interceptions                  48
Positioning                    48
Vision                         48
Penalties                      48
Composure                      48
Marking                        48
StandingTackle                 48
SlidingTackle                  48
GKDiving                       48
GKHandling                     48
GKKicking                      48
GKPositioning                  48
GKReflexes                     48
Release Clause               1564
Length: 89, dtype: int64


df.columns # listing the columns

Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause'],
      dtype='object')


# Plotting the Heatmap of the columns using correlation matrix
f,ax = plt.subplots(figsize=(25, 15))
sns.heatmap(df.corr(), annot=True, linewidths=0.5,linecolor="red", fmt= '.1f',ax=ax)
plt.show()


#Nationality Text Size = Nationality Player Count


# Plotiing the wordcloud for the Nationalit column
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='white',
                          width=1920,
                          height=1080
                         ).generate(" ".join(df.Nationality))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('graph.png')
plt.show()


#Imputing the missing values for the columns Club and Position
df['Club'].fillna('No Club', inplace = True)
df['Position'].fillna('ST', inplace = True)


# selecting columns to impute the missing values by mean
to_impute_by_mean = df.loc[:, ['Crossing', 'Finishing', 'HeadingAccuracy',
                                 'ShortPassing', 'Volleys', 'Dribbling', 'Curve', 'FKAccuracy',
                                 'LongPassing', 'BallControl', 'Acceleration', 'SprintSpeed',
                                 'Agility', 'Reactions', 'Balance', 'ShotPower', 'Jumping',
                                 'Stamina', 'Strength', 'LongShots', 'Aggression', 'Interceptions',
                                 'Positioning', 'Vision', 'Penalties', 'Composure', 'Marking',
                                 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
                                 'GKKicking', 'GKPositioning', 'GKReflexes']]


# replacing the missing values with mean
for i in to_impute_by_mean.columns:
    df[i].fillna(df[i].mean(), inplace = True)


'''These are categorical variables and will be imputed by mode.'''
to_impute_by_mode = df.loc[:, ['Body Type','International Reputation', 'Height', 'Weight', 'Preferred Foot','Jersey Number']]
for i in to_impute_by_mode.columns:
    df[i].fillna(df[i].mode()[0], inplace = True)


'''The following variables are either discrete numerical or continuous numerical variables.So the will be imputed by median.'''
to_impute_by_median = df.loc[:, ['Weak Foot', 'Skill Moves', ]]
for i in to_impute_by_median.columns:
    df[i].fillna(df[i].median(), inplace = True)


df.head(20)


'''Columns remaining to be imputed'''
df.columns[df.isna().any()]

Index(['Work Rate', 'Real Face', 'Joined', 'Loaned From',
       'Contract Valid Until', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Release Clause'],
      dtype='object')


df.fillna(0, inplace = True) # Filling the remaining  missing values with zero
df.head(20)


# functions to get the rounded values from different columns
def defending(data):
    return int(round((data[['Marking', 'StandingTackle', 
                               'SlidingTackle']].mean()).mean()))

def general(data):
    return int(round((data[['HeadingAccuracy', 'Dribbling', 'Curve', 
                               'BallControl']].mean()).mean()))

def mental(data):
    return int(round((data[['Aggression', 'Interceptions', 'Positioning', 
                               'Vision','Composure']].mean()).mean()))

def passing(data):
    return int(round((data[['Crossing', 'ShortPassing', 
                               'LongPassing']].mean()).mean()))

def mobility(data):
    return int(round((data[['Acceleration', 'SprintSpeed', 
                               'Agility','Reactions']].mean()).mean()))
def power(data):
    return int(round((data[['Balance', 'Jumping', 'Stamina', 
                               'Strength']].mean()).mean()))

def rating(data):
    return int(round((data[['Potential', 'Overall']].mean()).mean()))

def shooting(data):
    return int(round((data[['Finishing', 'Volleys', 'FKAccuracy', 
                               'ShotPower','LongShots', 'Penalties']].mean()).mean()))


# renaming a column
df.rename(columns={'Club Logo':'Club_Logo'}, inplace=True)


df.columns

Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club_Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause',
       'Defending', 'General', 'Mental', 'Passing', 'Mobility', 'Power',
       'Rating', 'Shooting'],
      dtype='object')


# adding these categories to the data

df['Defending'] = df.apply(defending, axis = 1)
df['General'] = df.apply(general, axis = 1)
df['Mental'] = df.apply(mental, axis = 1)
df['Passing'] = df.apply(passing, axis = 1)
df['Mobility'] = df.apply(mobility, axis = 1)
df['Power'] = df.apply(power, axis = 1)
df['Rating'] = df.apply(rating, axis = 1)
df['Shooting'] = df.apply(shooting, axis = 1)


# dataset after transformation
df.head(20)


# creating the players dataset
players = df[['Name','Defending','General','Mental','Passing',
                'Mobility','Power','Rating','Shooting','Flag','Age',
                'Nationality', 'Photo', 'Club_Logo', 'Club']]

players.head(20)


# different positions acquired by the players 

plt.figure(figsize = (18, 8))
plt.style.use('fivethirtyeight')
ax = sns.countplot('Position', data = df, palette = 'dark')
ax.set_xlabel(xlabel = 'Different Positions in Football', fontsize = 16)
ax.set_ylabel(ylabel = 'Count of Players', fontsize = 16)
ax.set_title(label = 'Comparison of Positions and Players', fontsize = 20)
plt.show()


# plotting coun of players based on their heights
plt.figure(figsize = (13, 8))
ax = sns.countplot(x = 'Height', data = df, palette = 'bone')
ax.set_title(label = 'Count of players on Basis of Height', fontsize = 20)
ax.set_xlabel(xlabel = 'Height in Foot per inch', fontsize = 16)
ax.set_ylabel(ylabel = 'Count', fontsize = 16)
plt.show()


# To show Different Work rate of the players participating in the FIFA 2019

plt.figure(figsize = (15, 7))
plt.style.use('_classic_test')

sns.countplot(x = 'Work Rate', data = df, palette = 'hls')
plt.title('Different work rates of the Players Participating in the FIFA 2019', fontsize = 20)
plt.xlabel('Work rates associated with the players', fontsize = 16)
plt.ylabel('count of Players', fontsize = 16)
plt.show()


x = df.Special
plt.figure(figsize = (12, 8))
plt.style.use('tableau-colorblind10')

ax = sns.distplot(x, bins = 58, kde = False, color = 'cyan')
ax.set_xlabel(xlabel = 'Special score range', fontsize = 16)
ax.set_ylabel(ylabel = 'Count of the Players',fontsize = 16)
ax.set_title(label = 'Histogram for the Speciality Scores of the Players', fontsize = 20)
plt.show()


# Every Nations' Player and their overall scores

some_countries = ('England', 'Germany', 'Spain', 'Argentina', 'France', 'Brazil', 'Italy', 'Columbia') # defining a tuple consisting of country names
data_countries = df.loc[df['Nationality'].isin(some_countries) & df['Overall']] # extracting the overall data of the countries selected in the line above
data_countries.head()


plt.rcParams['figure.figsize'] = (15, 7)
ax = sns.barplot(x = data_countries['Nationality'], y = data_countries['Overall'], palette = 'spring') # creating a bargraph
ax.set_xlabel(xlabel = 'Countries', fontsize = 9)
ax.set_ylabel(ylabel = 'Overall Scores', fontsize = 9)
ax.set_title(label = 'Distribution of overall scores of players from different countries', fontsize = 20)
plt.show()


df['Club'].value_counts().head(10) # finding the number of players in each club

No Club                    241
TSG 1899 Hoffenheim         33
Wolverhampton Wanderers     33
CD Leganés                  33
Southampton                 33
Burnley                     33
Rayo Vallecano              33
Manchester United           33
RC Celta                    33
Eintracht Frankfurt         33
Name: Club, dtype: int64


data = df.copy() # creating a copy dataset


# for visualizations
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')
sns.set(style="ticks")


some_clubs = ('CD Leganés', 'Southampton', 'RC Celta', 'Empoli', 'Fortuna Düsseldorf', 'Manchestar City',
             'Tottenham Hotspur', 'FC Barcelona', 'Valencia CF', 'Chelsea', 'Real Madrid') # creating a tuple of club names

data_clubs = data.loc[data['Club'].isin(some_clubs) & data['Overall']] # extracting the overall data of the clubs selected in the line above

data_clubs.head()


plt.rcParams['figure.figsize'] = (15, 8)
ax = sns.boxplot(x = data_clubs['Club'], y = data_clubs['Overall'], palette = 'inferno') # creating a boxplot
ax.set_xlabel(xlabel = 'Some Popular Clubs', fontsize = 9)
ax.set_ylabel(ylabel = 'Overall Score', fontsize = 9)
ax.set_title(label = 'Distribution of Overall Score in Different popular Clubs', fontsize = 20)
plt.xticks(rotation = 90)
plt.show()


# finding out the top 10 left footed footballers

left = data[data['Preferred Foot'] == 'Left'][['Name', 'Age', 'Club', 'Nationality']].head(10)
left


# finding out the top 10 Right footed footballers

right = data[data['Preferred Foot'] == 'Right'][['Name', 'Age', 'Club', 'Nationality']].head(10)
right


# comparing the performance of left-footed and right-footed footballers
# ballcontrol vs dribbing

sns.lmplot(x = 'BallControl', y = 'Dribbling', data = data, col = 'Preferred Foot')
plt.show()


data.groupby(data['Club'])['Nationality'].nunique().sort_values(ascending = False).head(10) # checking the clubs where players from the most number of nations play

Club
No Club                   28
Brighton & Hove Albion    21
Fulham                    19
Udinese                   18
Napoli                    18
Empoli                    18
Eintracht Frankfurt       18
West Ham United           18
AS Monaco                 18
Lazio                     18
Name: Nationality, dtype: int64


data.groupby(data['Club'])['Nationality'].nunique().sort_values(ascending = True).head(10) # checking the clubs where players from the least number of nations play

Club
Santos                       1
Ceará Sporting Club          1
América FC (Minas Gerais)    1
Paraná                       1
Chapecoense                  1
Padova                       1
Cittadella                   1
Sangju Sangmu FC             1
Ranheim Fotball              1
CA Osasuna                   1
Name: Nationality, dtype: int64


df.head()


df.drop(['Unnamed: 0'],axis=1,inplace=True) # dropping the unnamed column


df.head() # dataset after dropping column


#Player with maximum Potential and Overall Performance
player = str(df.loc[df['Potential'].idxmax()][1])
print('Maximum Potential : '+str(df.loc[df['Potential'].idxmax()][1]))
print('Maximum Overall Perforamnce : '+str(df.loc[df['Overall'].idxmax()][1]))

Maximum Potential : K. Mbappé
Maximum Overall Perforamnce : L. Messi


# finding the best players for each performance criteria

pr_cols=['Crossing', 'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys',
       'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl',
       'Acceleration', 'SprintSpeed', 'Agility', 'Reactions', 'Balance',
       'ShotPower', 'Jumping', 'Stamina', 'Strength', 'LongShots',
       'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties',
       'Composure', 'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving',
       'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes']
i=0
while i < len(pr_cols):
    print('Best {0} : {1}'.format(pr_cols[i],df.loc[df[pr_cols[i]].idxmax()][1]))
    i += 1

Best Crossing : K. De Bruyne
Best Finishing : L. Messi
Best HeadingAccuracy : Naldo
Best ShortPassing : L. Modrić
Best Volleys : E. Cavani
Best Dribbling : L. Messi
Best Curve : Quaresma
Best FKAccuracy : L. Messi
Best LongPassing : T. Kroos
Best BallControl : L. Messi
Best Acceleration : Douglas Costa
Best SprintSpeed : K. Mbappé
Best Agility : Neymar Jr
Best Reactions : Cristiano Ronaldo
Best Balance : Bernard
Best ShotPower : Cristiano Ronaldo
Best Jumping : Cristiano Ronaldo
Best Stamina : N. Kanté
Best Strength : A. Akinfenwa
Best LongShots : L. Messi
Best Aggression : B. Pearson
Best Interceptions : N. Kanté
Best Positioning : Cristiano Ronaldo
Best Vision : L. Messi
Best Penalties : M. Balotelli
Best Composure : L. Messi
Best Marking : A. Barzagli
Best StandingTackle : G. Chiellini
Best SlidingTackle : Sergio Ramos
Best GKDiving : De Gea
Best GKHandling : J. Oblak
Best GKKicking : M. Neuer
Best GKPositioning : G. Buffon
Best GKReflexes : De Gea


# creating a list of best players in each of the pr_cols criteria
i=0
best = []
while i < len(pr_cols):
    best.append(df.loc[df[pr_cols[i]].idxmax()][1])
    i +=1


best

['K. De Bruyne',
 'L. Messi',
 'Naldo',
 'L. Modrić',
 'E. Cavani',
 'L. Messi',
 'Quaresma',
 'L. Messi',
 'T. Kroos',
 'L. Messi',
 'Douglas Costa',
 'K. Mbappé',
 'Neymar Jr',
 'Cristiano Ronaldo',
 'Bernard',
 'Cristiano Ronaldo',
 'Cristiano Ronaldo',
 'N. Kanté',
 'A. Akinfenwa',
 'L. Messi',
 'B. Pearson',
 'N. Kanté',
 'Cristiano Ronaldo',
 'L. Messi',
 'M. Balotelli',
 'L. Messi',
 'A. Barzagli',
 'G. Chiellini',
 'Sergio Ramos',
 'De Gea',
 'J. Oblak',
 'M. Neuer',
 'G. Buffon',
 'De Gea']


# Plot to show the preferred foot choice of different players
f, ax = plt.subplots(figsize=(8, 6))
sns.countplot(x="Preferred Foot", hue="Real Face", data=df)
plt.show()


df.loc[df['Potential'].idxmax()][1] # Finding the player with the maximum potential

'K. Mbappé'


# showing the name of the players which occurs the most number of times from the first 20 names
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='black',
                          width=1920,
                          height=1080
                         ).generate(" ".join(df.Name[0:20]))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('players.png')
plt.show()


df.columns # all the columns in the dataset

Index(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag', 'Overall',
       'Potential', 'Club', 'Club_Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause',
       'Defending', 'General', 'Mental', 'Passing', 'Mobility', 'Power',
       'Rating', 'Shooting'],
      dtype='object')


# checking which clubs have been mentioned the most
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='black',
                          width=1920,
                          height=1080
                         ).generate(" ".join(df.Club))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('players.png')
plt.show()


# showing the name of the players which occurs the most number of times(left join)
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='black',
                          width=1920,
                          height=1080
                         ).generate(" ".join(left.Name))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('players.png')
plt.show()


#df.columns


# showing the name of the players which occurs the most number of times(right join)
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='white',
                          width=1920,
                          height=1080
                         ).generate(" ".join(right.Name))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('players.png')
plt.show()


# Checking which player has been mentioned the most in the 'best' list that we have prepared
plt.subplots(figsize=(25,15))
wordcloud = WordCloud(
                          background_color='white',
                          width=1920,
                          height=1080
                         ).generate(" ".join(best))
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('players.png')
plt.show()


import requests
import random
from math import pi

import matplotlib.image as mpimg
from matplotlib.offsetbox import (OffsetImage,AnnotationBbox)

# defining a method to show the details of a player
def details(row, title, image, age, nationality, photo, logo, club):
    
    flag_image = "img_flag.jpg"
    player_image = "img_player.jpg"
    logo_image = "img_club_logo.jpg"
     
    # obtaining the player image, flag image and logo image
    img_flag = requests.get(image).content
    with open(flag_image, 'wb') as handler:
        handler.write(img_flag)
    
    player_img = requests.get(photo).content
    with open(player_image, 'wb') as handler:
        handler.write(player_img)
     
    logo_img = requests.get(logo).content
    with open(logo_image, 'wb') as handler:
        handler.write(logo_img)
     
    # Defining the colour schemes
    r = lambda: random.randint(0,255)
    colorRandom = '#%02X%02X%02X' % (r(),r(),r())
    
    if colorRandom == '#ffffff':colorRandom = '#a5d6a7' # if random colour  is white, assign a different colour
    
    basic_color = '#37474f'
    color_annotate = '#01579b'
    
    img = mpimg.imread(flag_image)
    
    plt.figure(figsize=(15,8))
    categories=list(players)[1:]
    coulumnDontUseGraph = ['Flag', 'Age', 'Nationality', 'Photo', 'Logo', 'Club']
    N = len(categories) - len(coulumnDontUseGraph)
    
    # adjusting the angles to show different aspects in the graph
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]
    
    ax = plt.subplot(111, projection='polar') # sepcifying a  polar graph type
    ax.set_theta_offset(pi / 2) # set the offset in radians
    ax.set_theta_direction(-1) #the angle increases in the clockwise direction
    plt.xticks(angles[:-1], categories, color= 'black', size=17)
    ax.set_rlabel_position(0)
    plt.yticks([25,50,75,100], ["25","50","75","100"], color= basic_color, size= 10)
    plt.ylim(0,100)
    
    #creating the list of values which are not in (image, age, nationality, photo, logo, club) to show in the graph
    values = players.loc[row].drop('Name').values.flatten().tolist() 
    valuesDontUseGraph = [image, age, nationality, photo, logo, club]
    values = [e for e in values if e not in (valuesDontUseGraph)]
    values += values[:1]
    
    # customizing the graph attributes
    ax.plot(angles, values, color= basic_color, linewidth=1, linestyle='solid')
    ax.fill(angles, values, color= colorRandom, alpha=0.5)
    axes_coords = [0, 0, 1, 1]
    ax_image = plt.gcf().add_axes(axes_coords,zorder= -1)
    ax_image.imshow(img,alpha=0.5)
    ax_image.axis('off')
    
    # placeholders for showing nationality, age and team name
    ax.annotate('Nationality: ' + nationality.upper(), xy=(10,10), xytext=(103, 138),
                fontsize= 12,
                color = 'white',
                bbox={'facecolor': color_annotate, 'pad': 7})
                      
    ax.annotate('Age: ' + str(age), xy=(10,10), xytext=(43, 180),
                fontsize= 15,
                color = 'white',
                bbox={'facecolor': color_annotate, 'pad': 7})
    
    ax.annotate('Team: ' + club.upper(), xy=(10,10), xytext=(92, 168),
                fontsize= 12,
                color = 'white',
                bbox={'facecolor': color_annotate, 'pad': 7})

    # specifying the location for showing the image of player
    arr_img_player = plt.imread(player_image, format='jpg')
    imagebox_player = OffsetImage(arr_img_player)
    imagebox_player.image.axes = ax
    abPlayer = AnnotationBbox(imagebox_player, (0.5, 0.7),
                        xybox=(313, 223),
                        xycoords='data',
                        boxcoords="offset points"
                        )
    # specifying the location for showing the logo
    arr_img_logo = plt.imread(logo_image, format='jpg')
    
    imagebox_logo = OffsetImage(arr_img_logo)
    imagebox_logo.image.axes = ax
    abLogo = AnnotationBbox(imagebox_logo, (0.5, 0.7),
                        xybox=(-350, -246),
                        xycoords='data',
                        boxcoords="offset points"
                        )

    ax.add_artist(abPlayer)
    ax.add_artist(abLogo)

    plt.title(title, size=50, color= basic_color)


# defining a method to show the leading performers
def graphPolar(id = 0):
    if 0 <= id < len(data.ID):
        details(row = players.index[id], 
                title = players['Name'][id], 
                age = players['Age'][id], 
                photo = players['Photo'][id],
                nationality = players['Nationality'][id],
                image = players['Flag'][id], 
                logo = players['Club_Logo'][id], 
                club = players['Club'][id])
    else:
        print('The base has 17917 players. You can put positive numbers from 0 to 17917')


graphPolar(0)


graphPolar(1)


graphPolar(2)

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
0	0	158023	L. Messi	31	https://cdn.sofifa.org/players/4/19/158023.png	Argentina	https://cdn.sofifa.org/flags/52.png	94	94	FC Barcelona	...	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0	€226.5M
1	1	20801	Cristiano Ronaldo	33	https://cdn.sofifa.org/players/4/19/20801.png	Portugal	https://cdn.sofifa.org/flags/38.png	94	94	Juventus	...	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0	€127.1M
2	2	190871	Neymar Jr	26	https://cdn.sofifa.org/players/4/19/190871.png	Brazil	https://cdn.sofifa.org/flags/54.png	92	93	Paris Saint-Germain	...	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0	€228.1M
3	3	193080	De Gea	27	https://cdn.sofifa.org/players/4/19/193080.png	Spain	https://cdn.sofifa.org/flags/45.png	91	93	Manchester United	...	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0	€138.6M
4	4	192985	K. De Bruyne	27	https://cdn.sofifa.org/players/4/19/192985.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	92	Manchester City	...	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0	€196.4M
5	5	183277	E. Hazard	27	https://cdn.sofifa.org/players/4/19/183277.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	91	Chelsea	...	91.0	34.0	27.0	22.0	11.0	12.0	6.0	8.0	8.0	€172.1M
6	6	177003	L. Modrić	32	https://cdn.sofifa.org/players/4/19/177003.png	Croatia	https://cdn.sofifa.org/flags/10.png	91	91	Real Madrid	...	84.0	60.0	76.0	73.0	13.0	9.0	7.0	14.0	9.0	€137.4M
7	7	176580	L. Suárez	31	https://cdn.sofifa.org/players/4/19/176580.png	Uruguay	https://cdn.sofifa.org/flags/60.png	91	91	FC Barcelona	...	85.0	62.0	45.0	38.0	27.0	25.0	31.0	33.0	37.0	€164M
8	8	155862	Sergio Ramos	32	https://cdn.sofifa.org/players/4/19/155862.png	Spain	https://cdn.sofifa.org/flags/45.png	91	91	Real Madrid	...	82.0	87.0	92.0	91.0	11.0	8.0	9.0	7.0	11.0	€104.6M
9	9	200389	J. Oblak	25	https://cdn.sofifa.org/players/4/19/200389.png	Slovenia	https://cdn.sofifa.org/flags/44.png	90	93	Atlético Madrid	...	70.0	27.0	12.0	18.0	86.0	92.0	78.0	88.0	89.0	€144.5M

End of the Notebook¶